The exemplary embodiment relates to the field of image processing. It finds particular application in connection with the automated enhancement of digital images, and is described with particular reference thereto. However, it is to be appreciated that it may find more general application in image classification, image content analysis, image archiving, image database management and searching, and so forth.
Widespread availability of devices capable of acquiring digital images, such as digital cameras, cell phones, and other direct-digital imagers, and of optical scanners that convert film images, paper-printed images, and the like into digital format, has led to generation of large numbers of digital images. Regardless of the final medium in which the images will be managed, shared and visualized, the quality expectations of users are growing. Consumers are able to integrate their own image content into workflows, such as online photofinishing or content-sharing communities, and are making increasing use of automated or semi-automated image enhancement tools.
Image enhancements are generally applied to obtain a resulting image which is more suitable than the original for a specific objective. Visual quality is a sample objective, but depending on the application, quality might not be the main purpose of enhancement, e.g., in medical imaging.
For example, features such as automatic color balance or red-eye correction are now standard components in many image editing applications. Acquisition conditions, user expertise, compression algorithms and sensor quality can seriously degrade the final image quality. Image enhancement tools attempt to compensate for this degradation by altering image features for subsequent analysis, distribution or display. Examples of these image features include contrast and edge enhancement, noise filtering for a wide variety of noise sources, sharpening, exposure correction, color balance adjustment, automatic cropping, and correction of shaky images.
Some of these features, such as noise filtering, can be objectively defined and others, such as contrast, tend to be subjective and thus influenced by human perception. For example, while some people might prefer to see shadowed details made visible by application of a local contrast approach, others may appreciate the sensation of depth caused by the original shadows. Accordingly, there is an interest in capturing the intent of users in determining which automated image enhancements to apply. To some degree, the intention may vary according to the customer. A photographer wishing to depict a scene may value those enhancement operations that lead to a more faithful representation of the captured scene. A designer or an advertiser may look for image enhancements which are optimal for transmitting a message, e.g., an emotion. A person preparing a photograph of a baby for inclusion in a family album may wish to capture a facial expression, at the cost of leaving degradations untouched or even highlighting them. A photofinishing service generally wishes to automate image enhancements in order to please the largest possible audience.